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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## 数据探索"
   ]
  },
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    "scrolled": false,
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     "slide_type": "slide"
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Ever_Married</th>\n",
       "      <th>Age</th>\n",
       "      <th>Graduated</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Work_Experience</th>\n",
       "      <th>Spending_Score</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Var_1</th>\n",
       "      <th>Segmentation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462809</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Low</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>B</td>\n",
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       "      <th>3</th>\n",
       "      <td>461735</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>40</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Entertainment</td>\n",
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       "      <td>High</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Cat_6</td>\n",
       "      <td>A</td>\n",
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      "text/plain": [
       "       ID  Gender Ever_Married  Age Graduated     Profession  Work_Experience  \\\n",
       "0  462809    Male           No   22        No     Healthcare              1.0   \n",
       "1  462643  Female          Yes   38       Yes       Engineer              NaN   \n",
       "2  466315  Female          Yes   67       Yes       Engineer              1.0   \n",
       "3  461735    Male          Yes   67       Yes         Lawyer              0.0   \n",
       "4  462669  Female          Yes   40       Yes  Entertainment              NaN   \n",
       "\n",
       "  Spending_Score  Family_Size  Var_1 Segmentation  \n",
       "0            Low          4.0  Cat_4            D  \n",
       "1        Average          3.0  Cat_4            A  \n",
       "2            Low          1.0  Cat_6            B  \n",
       "3           High          2.0  Cat_6            B  \n",
       "4           High          6.0  Cat_6            A  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 加载数据\n",
    "train_data = pd.read_csv('../data/carclient_train.csv')\n",
    "test_data = pd.read_csv('../data/carclient_test.csv')\n",
    "\n",
    "# 显示数据的前5行\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:05:26.974719+00:00",
     "start_time": "2023-07-03T07:05:26.797718+00:00"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8068 entries, 0 to 8067\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   ID               8068 non-null   int64  \n",
      " 1   Gender           8068 non-null   object \n",
      " 2   Ever_Married     7928 non-null   object \n",
      " 3   Age              8068 non-null   int64  \n",
      " 4   Graduated        7990 non-null   object \n",
      " 5   Profession       7944 non-null   object \n",
      " 6   Work_Experience  7239 non-null   float64\n",
      " 7   Spending_Score   8068 non-null   object \n",
      " 8   Family_Size      7733 non-null   float64\n",
      " 9   Var_1            7992 non-null   object \n",
      " 10  Segmentation     8068 non-null   object \n",
      "dtypes: float64(2), int64(2), object(7)\n",
      "memory usage: 693.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据的基本信息\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "312BF3B151B7415F8057C8403EC39664",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
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    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 描述性统计信息  "
   ]
  },
  {
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   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:05:52.713602+00:00",
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       "      <th>count</th>\n",
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       "      <th>unique</th>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
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       "      <th>top</th>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Low</td>\n",
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       "      <td>Cat_6</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4417</td>\n",
       "      <td>4643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4968</td>\n",
       "      <td>2516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5238</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>463479.214551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.466906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.641663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.850123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2595.381232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.711696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.406763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.531413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>458982.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>461240.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>463472.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>465744.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                   ID Gender Ever_Married          Age Graduated Profession  \\\n",
       "count     8068.000000   8068         7928  8068.000000      7990       7944   \n",
       "unique            NaN      2            2          NaN         2          9   \n",
       "top               NaN   Male          Yes          NaN       Yes     Artist   \n",
       "freq              NaN   4417         4643          NaN      4968       2516   \n",
       "mean    463479.214551    NaN          NaN    43.466906       NaN        NaN   \n",
       "std       2595.381232    NaN          NaN    16.711696       NaN        NaN   \n",
       "min     458982.000000    NaN          NaN    18.000000       NaN        NaN   \n",
       "25%     461240.750000    NaN          NaN    30.000000       NaN        NaN   \n",
       "50%     463472.500000    NaN          NaN    40.000000       NaN        NaN   \n",
       "75%     465744.250000    NaN          NaN    53.000000       NaN        NaN   \n",
       "max     467974.000000    NaN          NaN    89.000000       NaN        NaN   \n",
       "\n",
       "        Work_Experience Spending_Score  Family_Size  Var_1 Segmentation  \n",
       "count       7239.000000           8068  7733.000000   7992         8068  \n",
       "unique              NaN              3          NaN      7            4  \n",
       "top                 NaN            Low          NaN  Cat_6            D  \n",
       "freq                NaN           4878          NaN   5238         2268  \n",
       "mean           2.641663            NaN     2.850123    NaN          NaN  \n",
       "std            3.406763            NaN     1.531413    NaN          NaN  \n",
       "min            0.000000            NaN     1.000000    NaN          NaN  \n",
       "25%            0.000000            NaN     2.000000    NaN          NaN  \n",
       "50%            1.000000            NaN     3.000000    NaN          NaN  \n",
       "75%            4.000000            NaN     4.000000    NaN          NaN  \n",
       "max           14.000000            NaN     9.000000    NaN          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据的描述性统计信息\n",
    "train_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "49C0883F65B842EE950E6775CD310E24",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 性别分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "A89B926B57F6491EAE20594D9DAE3A81",
    "jupyter": {},
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")               #忽略警告信息\n",
    "# 设置风格\n",
    "sns.set(style='whitegrid')\n",
    "# 绘制性别的计数图\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']      # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号\n",
    "plt.rcParams['figure.dpi']  = 100        #分辨率\n",
    "sns.countplot(x='Gender', data=train_data)\n",
    "plt.title('性别分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9EA6DA5063EB46BAA54FBDD3AE83E283",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 婚姻状况分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:07:07.717135+00:00",
     "start_time": "2023-07-03T07:07:07.340282+00:00"
    },
    "id": "3FD0612E01294A2D970AF9B4A7A4AC61",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制婚姻状况的计数图\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Ever_Married', data=train_data)\n",
    "plt.title('婚姻状况分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "340F6D103C524ABB8056C5A332427BF2",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 是否拥有学位分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:07:37.916726+00:00",
     "start_time": "2023-07-03T07:07:37.468852+00:00"
    },
    "id": "AD12038AC3D54ADEABD88B12850E47F5",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制是否拥有学位的计数图\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Graduated', data=train_data)\n",
    "plt.title('是否拥有学位分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "601554323CB3400BAD805C7882F1B3FB",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 职业分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:08:07.032527+00:00",
     "start_time": "2023-07-03T07:08:06.475794+00:00"
    },
    "id": "4D5D1708EE8B43AE87C51AFC816C6426",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#隐藏警告\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")               #忽略警告信息\n",
    "plt.rcParams['font.sans-serif']  = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号\n",
    "plt.rcParams['figure.dpi']  = 100        #分辨率\n",
    "\n",
    "# 绘制职业的计数图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(y='Profession', data=train_data)\n",
    "plt.title('职业分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5C7840E85A594C779F677E3D1251ABA1",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 工作经验分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:08:39.489954+00:00",
     "start_time": "2023-07-03T07:08:38.878193+00:00"
    },
    "id": "04F1BC06F7584AE399463582AD16BD6D",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制工作经验的直方图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(train_data['Work_Experience'].dropna(), kde=False, bins=30)\n",
    "plt.title('工作经验分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5C9C0E384D5F499683D26973D2F5DDD9",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 消费评分分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:09:11.234025+00:00",
     "start_time": "2023-07-03T07:09:10.800325+00:00"
    },
    "id": "121C98446D664102998FA61B0CE3927A",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制消费评分的计数图\n",
    "#隐藏警告\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")               #忽略警告信息\n",
    "plt.rcParams['font.sans-serif']  = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号\n",
    "plt.rcParams['figure.dpi']  = 100        #分辨率\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Spending_Score', data=train_data)\n",
    "plt.title('消费评分分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8EA3B343781D47548CFA619036F33195",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 家庭规模分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:10:01.030584+00:00",
     "start_time": "2023-07-03T07:10:00.354939+00:00"
    },
    "id": "35B7A59D88394EC9A504F18740B4FBD1",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LL+u3v/2tZs2apW9/+9taunSpJOnRRx/V6tWrNXfuXN18881xYWOz2bR69WoNHjxYt9566xnn+swzz5wy53A4LOnk5yBNnz69sX9lAIAmsEXr82JnAICRaq7y5OTkxLYdO3ZMP/nJTzRixAhNnjxZ7dq1q/P4cDgsp9Mpr9erYDCYkJeHnThx4owvVzuT48ePKyMjo879NZGWnJws6WS8eDweS26esGLFCj3zzDOxD6EFADQPwggAAACA8XiPEQAAAADjEUYAAAAAjEcYAQAAADBeg8Noy5YtmjhxonJzczVo0CAVFhbGPj/iuuuuU9++feN+VVRUxI5dvny5hg4dqoKCAq1YsSLuvB6PR7Nnz1ZeXp6uu+662Bt+AQAAAMBqDbpdt9fr1fTp03XNNdfoqaee0s6dOzVr1ix1795d3/3ud7Vjxw6tXbtW2dnZsWPS0tIkSS+99JKefPJJPfjgg+rWrZvmzZunrl276oorrlA0GtXcuXNls9n02muvafPmzZo9e7bWrFnTqDsPbdq0SdFoVC6Xq8HHAgAAAGg9QqGQbDZb7OMl6tKgMNq1a5fGjx8f+5yG9u3ba+DAgdq8ebO6dOmizp0764ILLqj12Oeee06TJ0/W6NGjJUnz589XcXGxrrjiCpWUlGjTpk1699131bNnT11wwQV6++23tW7dOl199dUNWaKk+A/xa27RaFShUEgul0s2m625l9PqMF9rMV9rMV9rMV9rMV9rMV/rMWNrtaT51rcLGhRGAwYM0IABA2JfRyIR7d69W0OGDFFJSYn8fr+GDx+u48eP65JLLtHPf/5z9evXT16vV3v37tWwYcNix+bl5Wnx4sWSpK1bt6pr165xH8aXl5en0tLSRoWRy+VSNBpVr169Gnxsovn9fpWVlalLly5KSUlp7uW0OszXWszXWszXWszXWszXWszXeszYWi1pvrt27apXnDUojL7u5Zdfltfr1bXXXqtf/vKXysvL07x585SSkqJHHnlEM2bM0Icffiiv1ytJ6tatW+zYdu3ayefzqaKiQpWVlerevXvcudPT07V169ZGry0UCmnbtm2NPj7RysrKmnsJrRrztRbztRbztRbztRbztRbztR4ztlZLma/b7T7jYxodRrt379YDDzygJUuWKCMjQw8//HDc/qVLl+rSSy/V+vXrdeGFF0qSkpKSYvtrPk3c7/fL6XSestjk5GT5/f7GLk8ul0u9e/du9PGJUlPLPXr0aPZabo2Yr7WYr7WYr7WYr7WYr7WYr/WYsbVa0nx37dpVr8c1KowqKio0Z84cXX311brmmmtqfUxKSorS0tJ04MABDR48WHa7XR6PR+np6ZIUu4rkcrmUmZkpj8cTd7zX661X2dXFZrMpNTW10ccnWkpKSotaT2vDfK3FfK3FfK3FfK3FfK3FfK3HjK3VEuZb3/c4Nfh23YFAQHPmzFHnzp1j7xEKBoMaO3asDh48GHvc3r175fF41K1bN7ndbuXm5mrDhg2x/aWlpUpNTVVGRoby8/O1ffv2uFt7l5aWqlOnTg1dHgAAAAA0WIPCKBqNauHChfJ4PLr33ntVVVWlEydOKBKJqGfPnrr99tu1efNmrV+/XgsWLIh91pEkjRs3TsuXL9ehQ4fk8/n09NNPa8SIEbLb7erVq5d69+4d+0yk0tJSffDBBxo1apQlTxoAAAAAvqpBL6XbsWOH/vjHP0qShg8fHts+aNAgPf7447rrrrs0depUZWZmasiQIbrlllvkcDgkSZMmTdLGjRs1evRoOZ1OZWVlqbCwMHaOe++9VzNnztTatWtVWVmpCRMmxP0ZAAAAAGCVBoVRTk6OduzYUef+Rx99tO4/yOlUUVGRtm3bJo/Ho4EDB8a9ESsnJ0dr167VJ598oqysLF100UUNWRoAAAAANFqTbtfdGDV3qKtNSkqKhg4dehZXAwAAAACNuPkCAAAAALQ2hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxjhnOdwOJp7CQAAADjHOZt7ATCb1xeULxBu9PGhUEg2d5qOVYbkDfji9qUmO9U21d3UJQIAAMAAhBGalS8Q1va9RxUKRxp1fCgYVPmhcnXsEJXL/Z8IcjntyumeRRgBAACgXggjNLtQONLoMAqGIwoEwwqGI5K9cecAAAAAeI8RAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMF6Dw2jLli2aOHGicnNzNWjQIBUWFioSiUiSPv74Y40ZM0bf/OY3tXjxYlVVVcWOq66u1v33369LL71UI0eO1O9///u485aVlemmm25SXl6eJk+erP379zfxqQEAAABA/TQojLxer6ZPn65vfetb+vjjj/XYY4+puLhYr7/+unbs2KE5c+Zo/PjxeuONN/Tll1+qsLAwduwjjzyiNWvWqKioSIWFhVq2bJm2bNkiSaqqqtLNN9+sjIwMvfnmmyooKNDcuXNjwQUAAAAAVmpQGO3atUvjx4/Xrbfeqvbt22vw4MEaOHCgNm/erOLiYuXm5mrmzJnq2rWr7rzzTr366quqqqpSMBjUCy+8oPnz52vQoEGxq0KrVq2SJL333ns6evSoli5dqm7dumnGjBny+/0qKSmx5EkDAAAAwFc5G/LgAQMGaMCAAbGvI5GIdu/erSFDhmjNmjW64oorYvs6dOigzMxM7dy5Uy6XSz6fT8OGDYvtz8vL05o1ayRJW7du1cUXX6z09PS4P6u0tFQDBw5s1BOLRqPy+XyNOjaR/H5/3O+IFwqFFAoGFQw37upgKBSK+z0mYlcoFGoR3wPnMr5/rcV8rcV8rcV8rcV8rceMrdWS5huNRmWz2c74uAaF0de9/PLL8nq9uvbaa7Vq1Sp17949bn96eroOHjyojIwMuVwuderUKbavXbt2OnDggCSpsrKy1mNr9jdGKBTStm3bGn18opWVlTX3Eloch8MhmztN5YfKFQiGm3QuzxFP3NfJbqc6Zdp0+EClqqurm3Ru8P1rNeZrLeZrLeZrLeZrPWZsrZYyX7fbfcbHNDqMdu/erQceeEBLlixRRkaGHA6HkpKS4h6TlJQkn8+n7OzsU/YlJyfHCtLhcJyy2KSkJB05cqSxy5PL5VLv3r0bfXyi+P1+lZWVqUePHkpJSWnu5bQ4xypD6tgh2qQrRp4jHmW3z5bL5Yptdzvtym6frcy0Tqc5GmfC96+1mK+1mK+1mK+1mK/1mLG1WtJ8d+3aVa/HNSqMKioqNGfOHF199dW65pprJEmZmZk6fPhw3OO8Xq/cbrcyMzPl9Xrl9/tjg6nZV3Ps3r174449ceJEvcquLjabTampqY0+PtFSUlJa1HpaCm/AJ5fbLdmbdqMNl8sV9/3ictrlcrmYeYLw/Wst5mst5mst5mst5ms9ZmytljDf+ryMTmrE7boDgYDmzJmjzp07a/HixbHt+fn52rhxY+xrr9erPXv2qHPnzuratavOO++8uP2lpaWxl9bl5+frk08+iXvJ01f3AwAAAICVGhRG0WhUCxculMfj0b333quqqiqdOHFCgUBA48aN07p167RhwwZJUlFRkTIzM5Wbmyu73a6rrrpKjz76qLxer44ePaoVK1Zo1KhRkqQhQ4aourpazz77rCRp3bp1+uyzz2L7AQAAAMBKDXop3Y4dO/THP/5RkjR8+PDY9kGDBqm4uFgLFizQtGnT1LZtW1VVVemxxx6T3X6yvebNm6fp06dr2LBhikQi6tWrl2bOnCnp5MugHnroIS1cuFDPPfecKioqNG/ePPXq1StRzxMAAAAA6tSgMMrJydGOHTvq3P/jH/9YY8aM0Y4dO9S/f39lZ2fH9rVt21YvvPCCPv30UwWDQQ0cOFBO53/++CFDhmjdunUqKSnR+eefTxQBAAAAOGuadLvu2nTu3FmdO3eudZ/dbld+fn6dx6alpenyyy9P9JIAAAAA4LQafPMFAAAAAGhtCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGa3AYRaNRzZs3T0VFRXHbr7vuOvXt2zfuV0VFRWz/8uXLNXToUBUUFGjFihVxx3o8Hs2ePVt5eXm67rrrtH379sY9GwAAAABoBGdDHhwMBnXXXXfp/fffV58+fWLbfT6fduzYobVr1yo7Ozu2PS0tTZL00ksv6cknn9SDDz6obt26ad68eeratauuuOIKRaNRzZ07VzabTa+99po2b96s2bNna82aNWrTpk2CniYAAAAA1K1BV4yWLFkih8OhvLy8uO2lpaXq3LmzLrjgArVr1y72y2azSZKee+45TZ48WaNHj1afPn00f/58FRcXS5JKSkq0adMmLVu2TBdccIGuvfZa9ezZU+vWrUvQUwQAAACA02vQFaOZM2eqe/fuuummm+K2l5SUyO/3a/jw4Tp+/LguueQS/fznP1e/fv3k9Xq1d+9eDRs2LPb4vLw8LV68WJK0detWde3aVT179ozbX1paqquvvrrRTywajcrn8zX6+ETx+/1xvyNeKBRSKBhUMBxp9PFf/T0mYlcoFGoR3wPnMr5/rcV8rcV8rcV8rcV8rceMrdWS5huNRmMXbE6nQWHUvXv3Wrfv3LlTeXl5mjdvnlJSUvTII49oxowZ+vDDD+X1eiVJ3bp1iz2+Xbt28vl8qqioUGVl5SnnTU9P19atWxuytFOEQiFt27atSedIpLKysuZeQovjcDhkc6ep/FC5AsFwk87lOeKJ+zrZ7VSnTJsOH6hUdXV1k87d2tjt9nr9cKjhcDi0b9++ej8+Go0qEmlc6JqKn7on1hgAACAASURBVA/WYr7WYr7WYr7WY8bWainzdbvdZ3xMg8KoLg8//HDc10uXLtWll16q9evX68ILL5QkJSUlxfYnJydLOlmQTqfzlIUmJyc3uS5dLpd69+7dpHMkgt/vV1lZmXr06KGUlJTmXk6Lc6wypI4dok26YuQ54lF2+2y5XK7YdrfTruz22cpM65SopbYagVBE/kD9YjEcDuv4l8eVkZ4hp7N+Py5Skh1KdnHDy/rg54O1mK+1mK+1mK/1mLG1WtJ8d+3aVa/HJSSMvi4lJUVpaWk6cOCABg8eLLvdLo/Ho/T0dEmKXUVyuVzKzMyUxxP/r/1er7deVXc6NptNqampTTpHIqWkpLSo9bQU3oBPLrdbsjftCoPL5Yr7nnE57XK5XMy8Ft6jPu3ef1yhesRoKBhU+aFydewQPfn3dAYup1053bOUms7cG4KfD9ZivtZivtZivtZjxtZqCfOt7ytlmvzPusFgUGPHjtXBgwdj2/bu3SuPx6Nu3brJ7XYrNzdXGzZsiO0vLS1VamqqMjIylJ+fr+3bt8fd2ru0tFSdOvEv/YBVQuFIvX4FwxEFgmEF6/n4+sQWAABAS9TkMHK73erZs6duv/12bd68WevXr9eCBQuUm5urQYMGSZLGjRun5cuX69ChQ/L5fHr66ac1YsQI2e129erVS71791ZhYaEikYhKS0v1wQcfaNSoUU1+cgAAAABQHwl5Kd3SpUt11113aerUqcrMzNSQIUN0yy23yOFwSJImTZqkjRs3avTo0XI6ncrKylJhYWHs+HvvvVczZ87U2rVrVVlZqQkTJmj48OGJWBoAAAAAnFGjwqjmM4hqpKen69FHH637D3E6VVRUpG3btsnj8WjgwIFxb8LKycnR2rVr9cknnygrK0sXXXRRY5YFAAAAAI1iyc0X6lJzh7rapKSkaOjQoWdxNQAAAABwEvfUPQvsdsYMAAAAtGRn9YqRqZLbpOtYZUjegM+S86cmO9U2tWm3NwcAAABMRhidBcFwVNvLjkp2R8LPXfO5MYQRAAAA0HiE0VkSCldL9vp9uBQAAACAs4s3vwAAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBADNyOFwNPcSAACAJGdzLwAATOD1BeULhOO2hUIh2dxpOlYZkjfga9L5U5OdapvqbtI5AAAwGWEEAGeBLxDW9r1HFQpHYttCwaDKD5WrY4eoXO7GR43LaVdO9yzCCACAJiCMAOAsCYUjcWEUDEcUCIYVDEcke+Q0RwIAAKvxHiMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxmtwGEWjUc2bN09FRUVx2z/++GONGTNG3/zmN7V48WJVVVXF9lVXV+v+++/XpZdeqpEjR+r3v/993LFlZWW66aablJeXp8mTJ2v//v2NfDoAAAAA0HANCqNgMKjbb79d77//ftz2HTt2aM6cORo/frzeeOMNffnllyosLIztf+SRR7RmzRoVFRWpsLBQy5Yt05YtWyRJVVVVuvnmm5WRkaE333xTBQUFmjt3riKRSAKeHgAAAACcWYPCaMmSJXI4HMrLy4vbXlxcrNzcXM2cOVNdu3bVnXfeqVdffVVVVVUKBoN64YUXNH/+fA0aNCh2VWjVqlWSpPfee09Hjx7V0qVL1a1bN82YMUN+v18lJSWJe5YAAAAAcBoNCqOZM2fqnnvukcvlitu+detWDRs2LPZ1hw4dlJmZqZ07d2rPnj3y+Xxx+/Py8lRaWho79uKLL1Z6enps/4ABA2L7AQAAAMBqzoY8uHv37rVur6ysPGVfenq6Dh48qIyMDLlcLnXq1Cm2r127djpw4MBpj63Z31jRaFQ+n69J50iEQCAgSQqFw4rKgpcHRuwKhUIt4rk2RigUUigYVDDcuNmEQqG432PO8blYqSEzr3O+dWHudapt7g2eb12Ye638fn/c70gs5mst5ms9ZmytljTfaDQqm812xsc1KIzq4nA4lJSUFLctKSlJPp9P2dnZp+xLTk6ODcnhcMjtdp9y7JEjR5q0plAopG3btjXpHIngcDhkc6fp6NEjCgTDCT9/stupTpk2HT5Qqerq6oSf30o1syk/VN7k2XiOeOK+PpfnYqXGzvzr860Lc6/dmeZe3/nWhbmfXllZWXMvoVVjvtZivtZjxtZqKfP9em/UJiFhlJmZqcOHD8dt83q9crvdyszMlNfrld/vV0pKSty+mmP37t0bd+yJEyfqtfjTcblc6t27d5POkQiBQED7Dh5TVlZ7RS24O7rbaVd2+2xlpnU684NboGOVIXXsEG3SFSPPEY+y22fHvcTzXJ+LlRoy87rmWxfmXrfa5t7Q+daFudfO7/errKxMPXr0iP33B4nDfK3FfK3HjK3Vkua7a9euej0uIWGUn5+vjRs3atKkSZJOhs+ePXvUuXNnde3aVeedd542btyo4cOHS5JKS0tjL63Lz8/X6tWrVV1dLYfDEdv/7W9/u0lrstlsSk1NbdI5EsnldEr2hIz7a+e1y+Vytajn2hDegE8ut1uyN+1lhi6XKy6mz/W5WKkxM//6fOt8HHOv0+nmXt/51oW5n15KSgqzsRDztRbztR4ztlZLmG99XkYnJegDXseNG6d169Zpw4YNkqSioiJlZmYqNzdXdrtdV111lR599FF5vV4dPXpUK1as0KhRoyRJQ4YMUXV1tZ599llJ0rp16/TZZ5/F9gMAAACA1RJyCSMnJ0cLFizQtGnT1LZtW1VVVemxxx6T3X6yu+bNm6fp06dr2LBhikQi6tWrl2bOnCnp5L+UPvTQQ1q4cKGee+45VVRUaN68eerVq1cilgYAAAAAZ9SoMCouLj5l249//GONGTNGO3bsUP/+/ZWdnR3b17ZtW73wwgv69NNPFQwGNXDgQDmd//mjhwwZonXr1qmkpETnn38+UQQAAADgrErom146d+6szp0717rPbrcrPz+/zmPT0tJ0+eWXJ3I5AAAAAFAvib9NGgAAAACcYwgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAArZbdzn/mAAD142zuBQAAYJXkNuk6VhmSN+Cz5PypyU61TXVbcm4AwNlFGAEAWq1gOKrtZUcluyPh53Y57crpnkUYAUArQRgBAFq1ULhastuaexkAgBaOF18DAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMF5Cw6i4uFh9+/aN+7VixQpJUmlpqSZMmKC8vDzNnTtXx48fjzt2+fLlGjp0qAoKCmLHAAAAAMDZkNAw+vTTTzV37lxt3Lgx9mvSpEnyeDy6+eabdckll+itt95Shw4dtHjx4thxL730kp588kn993//t55//nm9+OKL+sMf/pDIpQEAAABAnRIaRiUlJbrsssvUrl272C+3261XX31Vbdu21R133KGuXbtq0aJF2rBhgw4cOCBJeu655zR58mSNHj1affr00fz581VcXJzIpQEAAABAnZyJOtHBgwe1f/9+/epXv9I///lPdejQQZMnT9aUKVO0detWXXbZZXI6T/5xSUlJysnJUWlpqdLS0rR3714NGzYsdq68vLy4K0qNEY1G5fP5mnSORAgEApKkUDisqCKJ/wMidoVCoRbxXBsjFAopFAwqGG7cbEKhUNzvMef4XKzUkJnXOd+6MPc61Tb3Bs+3Lsy9Vvz8tZbf74/7HYnFfK3HjK3VkuYbjUZls9nO+LiEhdHWrVvVpUsX3XLLLcrJydFf/vIX3XnnnerWrZsqKyuVm5sb9/j09HQdOHBAXq9XktStW7fYvnbt2snn86miokLt2rVr1HpCoZC2bdvW+CeUIA6HQzZ3mo4ePaJAMJzw8ye7neqUadPhA5Wqrq5O+PmtVDOb8kPlTZ6N54gn7utzeS5WauzMvz7fujD32p1p7vWdb12Ye+34+Xt2lJWVNfcSWjXmaz1mbK2WMl+3233GxyQsjEaNGqVRo0bFvr722mu1fv16vfXWW3I4HEpKSop7fFJSkvx+vxwOR+zrGsnJyZJOFmZjw8jlcql3796NOjaRAoGA9h08pqys9opacBNAt9Ou7PbZykzrlPBznw3HKkPq2CHapCtGniMeZbfPlsvlim0/1+dipYbMvK751oW51622uTd0vnVh7rXj56+1/H6/ysrK1KNHD6WkpDT3clod5ms9ZmytljTfXbt21etxCQuj2nTo0EF///vfdf755+vw4cNx+7xer9xut9LT02W32+XxeJSenh7bJ6lJ/0fBZrMpNTW18YtPMJfTKdkTP26X0y6Xy9WinmtDeAM+udxuyd60l7m4XK64fwk41+dipcbM/OvzrfNxzL1Op5t7fedbF+Z+evz8tVZKSorxM7AS87UeM7ZWS5hvfV5GJyXw5guPPvqonnnmmbhtn3zyibp166b8/Hxt3Lgxtj0Siejzzz9Xp06d5Ha7lZubqw0bNsT2l5aWKjU1VRkZGYlaHgAAAADUKWFh1L9/f/3mN7/RBx98oC1btmjp0qXavHmzpkyZoiuvvFLbtm3T22+/Lenk5x1VVlaqoKBAkjRu3DgtX75chw4dks/n09NPP60RI0bIbufzZwEAAABYL6HvMZo/f75+9atfKRAIqG/fvlq5cqX69esnSbrnnnv0i1/8QsuWLVNFRYXuvvvu2EvnJk2apI0bN2r06NFyOp3KyspSYWFhopYGAAAAAKeV0Bdd33TTTbrppptq3Td+/HgVFBSotLRUffr0UZcuXf6zCKdTRUVF2rZtmzwejwYOHNjsb9ICAAAAYA5Lb77wde3bt9fIkSPr3H/hhReexdUAAAAAwEm8iQcAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAI3icDiaewkAkDDO5l4AAAA4+7y+oHyBcKOPD4VCsrnTdKwyJG/AF7cvNdmptqnupi4RAM4qwggAAAP5AmFt33tUoXCkUceHgkGVHypXxw5Rudz/iSCX066c7lmEEYBzDmEEAIChQuFIo8MoGI4oEAwrGI5I9sadAwBaEt5jBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAQAvkcDiaewmAUZzNvQAAAABTeH1B+QLhMz4uFArJ5k7TscqQvAFfvc6dmuxU21R3U5cIGIswAgAAOEt8gbC27z2qUDhy2seFgkGVHypXxw5Rudxnjh2X066c7lmEEdAEhBEAAMBZFApHzhhGwXBEgWBYwXBEsp/+sQASg/cYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAADASHxWFL6Ku9IBAACgVavt86Ma81lRteHzo+pmt59b12AIIwAAALRqtX1+VEM/K6o25/rnR9X3A4cbIxQKKblNuiXntgphBAAAgFbv658fxWdF1f8DhxslUq3stGjiz2shwggAAAAwVH0+cLhRItWSbIk/r4XOrRf+AQAAAIAFWlQYeTwezZ49W3l5ebruuuu0ffv25l4SAAAAAAO0mDCKRqOaO3eujh07ptdee0033XSTZs+erRMnTjT30gAAAAC0ci0mjEpKSrRp0yYtW7ZMF1xwga699lr17NlT69ata+6lAQAAAGjlbNFotEXcLqK4uFgrV66MC6HHH39cx44d05133tmgc5WUlCgajcrlciV6mQ0WjUYVro6outqiMdtscjvtstvPrTe31YhEoifvBtPIb8OopEikWna7I/7tfef4XKzUkJnXOd+6MPc61Tb3Bs+3Lsy9Vvz8PT1+/jaP+s6dn7+JZdnP4HN87k39OXAmDodNToddNlvzzicUCslmsyk/P/+0j2sxd6WrrKxU9+7d47alp6dr69atDT5XzfCb+y+hZg1uu11q/kZrkRwOm1IcTb1wyXAbouEzZ76JUPfcma9V+Pl7evz8bR4NmzvzTRR+BtcuMT8HWj6bzVavLmgxYeR0OuX+2odrJScny+/3N/hceXl5iVoWAAAAAAO0mETMzMyUx+OJ2+b1ek+JJQAAAABItBYTRvn5+dq+fbsqKipi20pLS9WpU6dmXBUAAAAAE7SYMOrVq5d69+6twsJCRSIRlZaW6oMPPtCoUaOae2kAAAAAWrkWc1c6Sdq+fbtmzpwpv9+vyspKTZgwQb/61a+ae1kAAAAAWrkWFUaS5Pf79cknnygrK0sXXXRRcy8HAAAAgAFaXBgBAAAAwNnWYt5jBAAAAADNhTACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAJQp1AopC1btmj37t3NvRQAAABLEUYWi0ajmjdvnoqKipp7Ka3Oli1bNHHiROXm5mrQoEEqLCxUJBJp7mW1GqWlpRo5cqRuu+02/eAHP9DEiRNVWVnZ3Mtqld544w2NGjWquZfRqhQXF6tv375xv1asWNHcy2p1du7cqQEDBmjz5s3NvZRWY/Xq1ad879b8QmJ89NFHGjNmjPr3769Ro0bpd7/7XXMvqVX505/+pEmTJik/P19jx47VO++809xLqjdncy+gNQsGg7rrrrv0/vvvq0+fPs29nFbF6/Vq+vTpuuaaa/TUU09p586dmjVrlrp3764JEyY09/LOeeFwWAsXLtSCBQt0ww03yOv1asKECVq1apVmzJjR3MtrVQ4ePKh77rlHaWlpzb2UVuXTTz/V3LlzNWXKlNi25OTkZlxR6xMOh7Vo0SJ973vf0yWXXNLcy2k1xo4dq9GjR8dte/rpp7Vz585mWlHr8u9//1u33nqrCgsLlZOTo02bNunWW29Vjx49NHz48OZe3jnv888/16xZs3T77berqKhIf/7zn7Vo0SJFIhGNGzeuuZd3RlwxstCSJUvkcDiUl5fX3EtpdXbt2qXx48fr1ltvVfv27TV48GANHDiQf7VMkIqKiv/f3v3HVFX/fwB/qsCMgEUmECGXWxgJ40Lj50UyvagsgS6QliN0SPzaICLIIJfeNaRAWISuKyU/jagYYMn4Ubs1zcWNphQyYUCANURbGIY4cQL388dnn7vuVz/F3efi+XLu87Hdjfs+5/2+z7ux3b3Oeb/fB7t27cKOHTsAADY2NnB3d8fk5KTAycRFp9PhjTfegJOTk9BRRKe7uxtyuRx2dnb6l5WVldCxREWtVmNqagpZWVlCRxEVKysrg//bmZkZNDQ0IDc3V+hootDX1wdXV1c89dRTWL16NbZu3YrHHnsMIyMjQkcThcbGRgQEBCA+Ph6rVq2CUqmEQqFAW1ub0NEWhIXRIkpLS0NBQQEsLS2FjiI6vr6+Bj8S8/PzGB4ehlQqFTCVeDz44INISEjQvz979iw6OzvxzDPPCBdKhOrq6nDp0iXk5OQIHUVUrly5gvHxceTn58Pb2xthYWGora0VOpaoXLhwAR988AE2b96M1tZWrkNcRGq1GuHh4XB3dxc6iig8/vjjGBoawunTpzEzMwONRoOff/4ZoaGhQkcThT/++ANr1qwxaLO0tISFxdKYpLY0Ui5REolE6Ahm47PPPsP09DRiYmKEjiI6UVFRGBwcRGZmJqfLmNDo6Cjee+89VFdX4+bNm0LHEZW+vj488sgjyM7OxhNPPIHOzk7s378frq6u2LRpk9DxljydTgeVSgVra2vodDoMDAzg0KFDSEtLQ2JiotDxROXq1av4/PPP0dzcLHQU0XBzc0NKSgpSUlL0bSqVioWnichkMnz88ceYmpqCnZ0dxsbGcOrUKezbt0/oaAvCwoiWvOHhYRw6dAgqlQoPPPCA0HFEp6KiAi0tLSgrK4OPjw+vqpnA3NwccnNzkZiYCJlMhq6uLqEjiYpCoTDYzCImJgZarRYnT55kYWQC3d3d6O3thVqtRlhYGAAgMDAQr7zyCpRKJVatWiVwQvGoq6uDXC7Ho48+KnQU0ejv70dVVRWOHDmC9evXo6+vD3v37oW9vT1nRZhAfHw8zp8/j6ioKHh7e+OHH36AnZ3dklhfBHAqHS1xU1NTSE9Ph1KpRHR0tNBxRMnR0RFJSUmIiorCJ598InQcUSgvL8fy5cuRlpYmdBSz4eDggMuXLwsdQxTGx8dhYWGBjRs36ttkMhnm5ubw66+/ChdMZObn59Hc3MyZECb2xRdfICQkBFu3bsX999+PgIAA7Nq1C/X19UJHE4WVK1fi8OHDaGhoQExMDP7880+89tprS2aNJwsjWrJmZmaQnp4OZ2dnvPnmm0LHEZW+vj5kZmZCp9Pp26ysrLBixQoBU4lHU1MT+vv7ERQUBH9/f6SlpWF8fBz+/v44e/as0PGWvLKyMlRUVBi0nTt3Dq6urgIlEpeHH34Y8/PzmJmZ0beNjY0B+PeFFDKN77//Hjdu3DAoQOl/Nzs7i4mJCYO2q1ev8nEfJubo6IiOjg4EBAQgIiJC6DgLxql0tCTpdDq8+uqrmJiYQE1NDW7duoVbt25hxYoV3JLXBNzc3NDd3Y233noLKSkpGBoaQktLCw4dOiR0NFGor6/H7Oys/n1PTw+KiopQX1+P1atXC5hMHLy9vZGbmwuJRAJnZ2ecOHECPT09vIBiIr6+vpBIJDhw4ACysrIwNTWFgoIChISEwNnZWeh4oqHRaBAYGLhkrrQvFcHBwairq0NJSQk8PT0xNDSE48eP4/XXXxc6mqj09vaivb19ya2PY2FES9LAwAC++eYbADB47kBgYCA++ugjoWKJhrW1NSoqKlBQUIBt27bByckJ+/fv168noP/N/92e+9KlS7CwsICLi4tAicRFoVAgMzMT+fn5mJmZgYeHB2pra+Hl5SV0NFGwsLBAZWUlioqK8Nxzz+H27duQy+XIz88XOpqonDlzBi+++KLQMURn8+bN2LdvH+rr61FbWwtbW1skJSUhPj5e6GiiodPpcPDgQezevXvJPcdzme6vc2WIiIiIiIjMENcYERERERGR2WNhREREREREZo+FERERERERmT0WRkREREREZPZYGBERERERkdljYURERERERGaPhRERES1IV1cXPDw87voqLS016Wfl5eUhLy/vH7MsFq1Wi9jYWMhkMoSHh6Ojo+Ou5ykUiiX3AEMiIro7PuCViIiMUlJSAjc3N4M2BwcHk35GRkaGScczxsDAAJKTk5GSkoLc3Fw0NTUhJycHnp6ecHV1NTj36NGjJv/uREQkDBZGRERkFHd3d6xbt25RP8PFxWVRx/87zc3NkEqlyMzMBAA8+eSTOHXqFE6ePHlHwbaYd62IiOjeAdkpBwAABR5JREFUYmFERET0F5OTk7h+/TpmZ2dhYWEBKysrVFRUwNbWVuhoRES0iLjGiIiITKampgZhYWHw8fGBUqlEZ2cnAGBsbAweHh6orq6GXC5HZGQktFotNm3ahNDQUJw/f95gnH9aY/TfJCYmIjU11aBNrVYjODgYt2/fXtAYTz/9NC5fvozU1FQMDQ0BAGQyGaRS6R3n/rc1RgqF4o51WAqFwuCctrY2REZGQiaTITo6GlqtdqFfk4iIFgELIyIiMomWlha88847iIuLQ2VlJfz8/JCZmYnp6Wn9OV9//TWKioowOjqKvXv3QqVSwd7eHidOnDBJBqVSie+++w6Tk5P6tvb2dkRGRsLS0nJBY0RERCA7OxtdXV2IiopCdnY2rly5YlSOo0ePorGxEY2NjaiuroatrS2CgoL0x7VaLbKzsxEeHo7Kykr4+PggOTkZw8PDRn0OERGZDgsjIiIySnR0tMGdkJ9++gkA4OjoiOLiYrz00kvw9/fHjh07cP36dYyMjOj7ZmdnY8OGDXBwcEB8fDw2btwIT09P3Lx50yTZtmzZAktLS3z55ZcAgOHhYQwODiImJsaocVJTU6HRaBAXF4evvvoK27dvx8WLFxfc38PDA97e3vDy8kJNTQ1cXFygUqn0x9VqNRQKBV5++WUEBARApVLBxsYGra2tRuUkIiLT4RojIiIySmlpqcG0sv/sUBcYGIhvv/0WKpUKP/74o/7ux1+Lnv/s4LZs2TKDv03F2toaW7ZsQWtrK3bu3Im2tjasXbsWXl5eRo/l5OSEAwcO4Nlnn8WePXtQWFiI8vJyo8Y4fPgwenp60NTUhJUrV+rbBwcHce3atTs2b/jll1+MzklERKbBwoiIiIwilUrvuitdcXEx6uvr8cILLyAtLQ1+fn7YsGHDPc+nVCqRlJSE3377De3t7YiNjTWqf2xsLHbv3o3o6GgAgK+vL2JiYqDRaIwaR6PR4NixY/jwww/vusteXFwcnn/+eYM2bvBARCQcTqUjIiKTaGhoQEJCAvLy8rBt2zaDtUX3klwux0MPPYTS0lJcvHgRUVFRRvWfnp5GV1eXQdu1a9fg7Oy84DFGRkaQm5uLrKwsrF+//o7ja9euxe+//45169bpXxqNBqdPnzYqKxERmQ7vGBERkUnY29tDq9UiJCQEo6OjOHLkCABgbm7unuZYvnw5IiMjUVVVhdDQUDg6OhrVf+fOnSgpKYFEIoGfnx+6u7vR0dGB4uLiBfWfmZlBRkYGJBIJgoOD0dvbqz8mlUphY2OD9PR07NmzB6WlpQgNDUV3dzfef/99lJWVGZWViIhMh4URERGZRGFhIfLz85GcnAyJRIKcnBwUFhbi3LlzcHV1vadZlEolqqqqjN50AQASEhKwbNkyfPrppygvL8eaNWtQUFCAiIiIBfWfmJjQr6/avn27wbHjx48jKCgIcrkc7777LtRqNaqqquDi4oK3334b4eHhRuclIiLTWKbT6XRChyAiIjKF6elp9PT04MKFCzh27BjOnDljsOnB7Ozs3/a3sOD1QiIic8VfACIiEo0bN24gIyMD9913Hw4ePGhQFAH4x93pGhsb4e3tvZgRiYjo/yneMSIiIrPR39//t8elUukdxRQREZkHFkZERERERGT2uF03ERERERGZPRZGRERERERk9lgYERERERGR2WNhREREREREZo+FERERERERmT0WRkREREREZPZYGBERERERkdljYURERERERGbvXy5xd+REQradAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制家庭规模的直方图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.distplot(train_data['Family_Size'].dropna(), kde=False, bins=30)\n",
    "plt.title('家庭规模分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EBF706DF56F24D99A1026081650DD0F7",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 匿名分组分布  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:10:30.473330+00:00",
     "start_time": "2023-07-03T07:10:29.999627+00:00"
    },
    "id": "57B98A2327684051A79A511C91C775D6",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#隐藏警告\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")               #忽略警告信息\n",
    "plt.rcParams['font.sans-serif']  = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号\n",
    "plt.rcParams['figure.dpi']  = 100        #分辨率\n",
    "\n",
    "# 绘制匿名分组的计数图\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Var_1', data=train_data)\n",
    "plt.title('匿名分组分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "274B7E3E76BA4CE4A3E4C9E5F15E86FF",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 客户分类分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:11:04.043665+00:00",
     "start_time": "2023-07-03T07:11:03.599574+00:00"
    },
    "id": "341F2DC06D8A4D17A8A1289E60F06C5E",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制客户分类的计数图\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Segmentation', data=train_data)\n",
    "plt.title('客户分类分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3E191D65A6544A31992E4BCD1B1D4237",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 数据预处理  \n",
    "\n",
    "在上面的步骤中，我们对数据进行了以下预处理：  \n",
    "\n",
    "1. 填充了缺失值：对于分类变量，我们使用了最常见的类别进行填充；对于数值变量，我们使用了中位数进行填充。  \n",
    "2. 将分类变量转换为数值：对于二元分类变量，我们使用了0和1进行编码；对于多元分类变量，我们使用了独热编码。  \n",
    "\n",
    "预处理后的数据如上所示。  \n",
    "\n",
    "接下来，我们将进行模型训练和预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:12:14.075337+00:00",
     "start_time": "2023-07-03T07:12:13.797288+00:00"
    },
    "datalink": {
     "05fe2793-3ea2-4cc0-805d-689c490bab32": {
      "applied_filters": [],
      "dataframe_info": {
       "default_index_used": true,
       "orig_num_cols": 25,
       "orig_num_rows": 5,
       "orig_size_bytes": 480,
       "truncated_num_cols": 25,
       "truncated_num_rows": 5,
       "truncated_size_bytes": 480,
       "truncated_string_columns": []
      },
      "display_id": "05fe2793-3ea2-4cc0-805d-689c490bab32",
      "dx_settings": {
       "ALLOW_NOTEABLE_ATTRS": true,
       "COLUMN_SAMPLING_METHOD": "outer",
       "DB_LOCATION": ":memory:",
       "DEV_MODE": false,
       "DISPLAY_MAX_COLUMNS": 100,
       "DISPLAY_MAX_ROWS": 50000,
       "DISPLAY_MODE": "simple",
       "ENABLE_ASSIGNMENT": true,
       "ENABLE_DATALINK": true,
       "FLATTEN_COLUMN_VALUES": true,
       "FLATTEN_INDEX_VALUES": false,
       "GENERATE_DEX_METADATA": false,
       "HTML_TABLE_SCHEMA": false,
       "LOG_LEVEL": 30,
       "MAX_RENDER_SIZE_BYTES": 104857600,
       "MAX_STRING_LENGTH": 250,
       "NUM_PAST_SAMPLES_TRACKED": 3,
       "RANDOM_STATE": 12648430,
       "RESET_INDEX_VALUES": false,
       "ROW_SAMPLING_METHOD": "random",
       "SAMPLING_FACTOR": 0.1,
       "SAMPLING_METHOD": "random",
       "STRINGIFY_COLUMN_VALUES": true,
       "STRINGIFY_INDEX_VALUES": false
      },
      "sample_history": [],
      "sampling_time": "2023-07-03T07:12:13.910606",
      "user_variable_name": null,
      "variable_name": "unk_dataframe_7ca0e684b1a04ad29c8cf09fe0d63686"
     }
    },
    "id": "2377FA9CAF734604AED7219EA9F142F1",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
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       "      <th>Profession_Artist</th>\n",
       "      <th>...</th>\n",
       "      <th>Profession_Homemaker</th>\n",
       "      <th>Profession_Lawyer</th>\n",
       "      <th>Profession_Marketing</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466315</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461735</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>462669</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Gender  Ever_Married  Age  Graduated  Work_Experience  \\\n",
       "0  462809       0             0   22          0              1.0   \n",
       "1  462643       1             1   38          1              1.0   \n",
       "2  466315       1             1   67          1              1.0   \n",
       "3  461735       0             1   67          1              0.0   \n",
       "4  462669       1             1   40          1              1.0   \n",
       "\n",
       "   Spending_Score  Family_Size  Segmentation  Profession_Artist  ...  \\\n",
       "0               0          4.0             3                  0  ...   \n",
       "1               1          3.0             0                  0  ...   \n",
       "2               0          1.0             1                  0  ...   \n",
       "3               2          2.0             1                  0  ...   \n",
       "4               2          6.0             0                  0  ...   \n",
       "\n",
       "   Profession_Homemaker  Profession_Lawyer  Profession_Marketing  Var_1_Cat_1  \\\n",
       "0                     0                  0                     0            0   \n",
       "1                     0                  0                     0            0   \n",
       "2                     0                  0                     0            0   \n",
       "3                     0                  1                     0            0   \n",
       "4                     0                  0                     0            0   \n",
       "\n",
       "   Var_1_Cat_2  Var_1_Cat_3  Var_1_Cat_4  Var_1_Cat_5  Var_1_Cat_6  \\\n",
       "0            0            0            1            0            0   \n",
       "1            0            0            1            0            0   \n",
       "2            0            0            0            0            1   \n",
       "3            0            0            0            0            1   \n",
       "4            0            0            0            0            1   \n",
       "\n",
       "   Var_1_Cat_7  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据预处理\n",
    "\n",
    "# 填充缺失值\n",
    "train_data['Ever_Married'].fillna(train_data['Ever_Married'].mode()[0], inplace=True)\n",
    "train_data['Graduated'].fillna(train_data['Graduated'].mode()[0], inplace=True)\n",
    "train_data['Profession'].fillna(train_data['Profession'].mode()[0], inplace=True)\n",
    "train_data['Work_Experience'].fillna(train_data['Work_Experience'].median(), inplace=True)\n",
    "train_data['Family_Size'].fillna(train_data['Family_Size'].median(), inplace=True)\n",
    "train_data['Var_1'].fillna(train_data['Var_1'].mode()[0], inplace=True)\n",
    "\n",
    "# 将分类变量转换为数值\n",
    "train_data['Gender'] = train_data['Gender'].map({'Male': 0, 'Female': 1})\n",
    "train_data['Ever_Married'] = train_data['Ever_Married'].map({'No': 0, 'Yes': 1})\n",
    "train_data['Graduated'] = train_data['Graduated'].map({'No': 0, 'Yes': 1})\n",
    "train_data['Spending_Score'] = train_data['Spending_Score'].map({'Low': 0, 'Average': 1, 'High': 2})\n",
    "train_data['Segmentation'] = train_data['Segmentation'].map({'A': 0, 'B': 1, 'C': 2, 'D': 3})\n",
    "\n",
    "# 对Profession和Var_1进行独热编码\n",
    "train_data = pd.get_dummies(train_data, columns=['Profession', 'Var_1'])\n",
    "\n",
    "# 显示处理后的数据\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DDA208D8EFBF4B61AA655C417273F7C6",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## 模型训练和预测  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:13:13.745681+00:00",
     "start_time": "2023-07-03T07:13:12.497059+00:00"
    },
    "id": "E458F0D6A1E34F248E92ABDACD07C387",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林分类器预测结果评价报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.37      0.37       391\n",
      "           1       0.36      0.34      0.35       369\n",
      "           2       0.49      0.51      0.50       380\n",
      "           3       0.63      0.65      0.64       474\n",
      "\n",
      "    accuracy                           0.48      1614\n",
      "   macro avg       0.46      0.47      0.47      1614\n",
      "weighted avg       0.47      0.48      0.48      1614\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# 定义特征和目标变量\n",
    "X = train_data.drop(['ID', 'Segmentation'], axis=1)\n",
    "y = train_data['Segmentation']\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建随机森林分类器\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "# 训练模型\n",
    "clf.fit(X_train, y_train)\n",
    "# 预测测试集\n",
    "y_pred = clf.predict(X_test)\n",
    "print('随机森林分类器预测结果评价报告：\\n',classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "056FB1226E194FAEA184408FA37F778B",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 随机森林分类器的混淆矩阵  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:17:14.678617+00:00",
     "start_time": "2023-07-03T07:17:14.097478+00:00"
    },
    "id": "46DB910C5997409B94A41BAEDE4FE37E",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 计算混淆矩阵\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix for Random Forest Classifier')\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 极端随机树分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:18:03.107324+00:00",
     "start_time": "2023-07-03T07:18:02.097730+00:00"
    },
    "id": "B00CFFD93F8F48E3BEA0774993B32ADF",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "极端随机森林分类器预测结果评价报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.37      0.38      0.37       391\n",
      "           1       0.33      0.33      0.33       369\n",
      "           2       0.48      0.47      0.47       380\n",
      "           3       0.61      0.62      0.62       474\n",
      "\n",
      "    accuracy                           0.46      1614\n",
      "   macro avg       0.45      0.45      0.45      1614\n",
      "weighted avg       0.46      0.46      0.46      1614\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# 创建极端随机树分类器\n",
    "clf_ext = ExtraTreesClassifier(n_estimators=100, random_state=42)\n",
    "# 训练模型\n",
    "clf_ext.fit(X_train, y_train)\n",
    "# 预测测试集\n",
    "y_pred_ext = clf_ext.predict(X_test)\n",
    "print('极端随机森林分类器预测结果评价报告：\\n',classification_report(y_test, y_pred_ext))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0D46AC0AD1EE456097B5E5BEFF19A9DC",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 极端随机树分类器的混淆矩阵  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:18:21.593161+00:00",
     "start_time": "2023-07-03T07:18:21.041176+00:00"
    },
    "id": "E1717F0261554CD5A8295561F876E00C",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算混淆矩阵\n",
    "cm_ext = confusion_matrix(y_test, y_pred_ext)\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_ext, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix for Extra Trees Classifier')\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 支持向量机分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:19:34.934343+00:00",
     "start_time": "2023-07-03T07:19:30.326993+00:00"
    },
    "id": "0551570E23EE4CE1884B053E06192EAA",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "支持向量机分类器预测结果评价报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.39      0.47      0.43       391\n",
      "           1       0.32      0.05      0.08       369\n",
      "           2       0.41      0.64      0.50       380\n",
      "           3       0.63      0.66      0.65       474\n",
      "\n",
      "    accuracy                           0.47      1614\n",
      "   macro avg       0.44      0.46      0.41      1614\n",
      "weighted avg       0.45      0.47      0.43      1614\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# 创建支持向量机分类器\n",
    "clf_svc = SVC(random_state=42)\n",
    "# 训练模型\n",
    "clf_svc.fit(X_train, y_train)\n",
    "# 预测测试集\n",
    "y_pred_svc = clf_svc.predict(X_test)\n",
    "print('支持向量机分类器预测结果评价报告：\\n',classification_report(y_test, y_pred_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3191AAD1F7BF4707925C980BB159F1AD",
    "jupyter": {},
    "noteable": {
     "cell_type": "markdown"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "runtime": {
     "execution_status": null,
     "status": "default"
    },
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 支持向量机分类器的混淆矩阵  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-03T07:19:55.461579+00:00",
     "start_time": "2023-07-03T07:19:54.856308+00:00"
    },
    "id": "F89D3FF0A9334467AE22A6C5A31DA2AB",
    "jupyter": {},
    "noteable": {
     "cell_type": "code"
    },
    "notebookId": "64a27f198cc3867397d96b27",
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算混淆矩阵\n",
    "cm_svc = confusion_matrix(y_test, y_pred_svc)\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_svc, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix for Support Vector Machine Classifier')\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
